Estimating the Values of Missing Data in Railway Networks Using their Spatial Correlation

In railway infrastructure management, it is always desired to know the conditionof each track segment, which is represented approximately by the conditionindicators. The values of condition indicators can be used as a decisioncriterion about whether the intervention on the track should be implementedin the future or when the intervention should be executed. However, the informationis not sufficient to draw a decision or a prediction when working withthe condition indicator values. This problem can happen due to collection errorswhen inspecting the track or the inconsistency of the data storage formatin different years or in different notations. Therefore, fulfilling the competenceof the track segment condition data sets by estimating the missing data tosupport the decision-making process is important. In this paper, experimentsof different models that can utilize the spatial correlation among data pointsare done to investigate their estimation ability. The models include the Kriging,Co-Kriging, ANN-Kriging hybrid model and Bi-LSTM neural network,which are all having the ability to model the data with spatial correlation or asequential relationship. The condition indicator values of the track segmentsare used and serval auxiliary variables correlated to the condition values arealso included. The results show that the condition values could be estimatedwith reasonably low estimation errors based on their potential correlation

Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach

The main objective of this study is to explore the spatiotemporal activities pattern of bicycle sharing system by combining together temporal and spatial attributes variables through clustering analysis method. Specifically, three clustering algorithms, i.e., hierarchical clustering, K-means clustering, expectation maximization clustering, are chosen to group the bicycle sharing stations. The temporal attributes variables are obtained through the statistical analysis of bicycle sharing smart card data, and the spatial attributes variables are quantified by point of interest (POI) data around bicycle sharing docking stations, which reflects the influence of land use on bicycle sharing system. According to the performance of the three clustering algorithms and six cluster validation measures, K-means clustering has been proven as the better clustering algorithm for the case of Ningbo, China. Then, the 477 bicycle sharing docking stations were clustered into seven clusters. The results show that the stations of each cluster have their own unique spatiotemporal activities pattern influenced by people’s travel habits and land use characteristics around the stations. This analysis will help bicycle sharing operators better understand the system usage and learn how to improve the service quality of the existing system

Measuring Bikeshare Access/Egress Transferring Distance and Catchment Area around Metro Stations from Smartcard Data

Metro⁻bikeshare integration is considered a green and efficient travel model. To better develop such integration, it is necessary to monitor and analyze metro⁻bikeshare transfer characteristics. This paper measures access and egress transferring distances and catchment areas based on smartcard data. A cubic regression model is conducted for the exploration of the 85th access and egress network-based transferring distance around metro stations. Then, the independent samples t-test and one-way analysis of variance (ANOVA) are used to explore access and egress transfer characteristics in demographic groups and spatial and temporal dimension. Additionally, the catchment area is delineated by applying both the network-based distance method and Euclidean distance method. The result reveals that males outcompete females both in access and egress distances and urban dwellers ride a shorter distance than those in suburban areas. Access and egress distances are both shorter in morning peak hours than those in evening peak hours and access distance on weekdays is longer than that on weekends. In addition, network-based catchment area accounts for over 90% of Euclidean catchment area in urban areas, while most of the ratios are less than 85% in suburban. The paper uses data from Nanjing, China as a case study. This study serves as a scientific basis for policy makers and bikeshare companies to improve metro⁻bikeshare integration

Impacts of storm wave-induced coastal hazards on the coast of China

The mainland coast of China is 18,000km long and houses more than 50% of its population. There are increasing pressures to develop the Chinese coastal areas for residential, commercial, tourism and recreational purposes. However, the coast of China has been periodically ravaged by tropical cyclones, storm surges and large coastal waves, resulting in heavy losses of coastal economy and human lives. Three individual extreme tropical cyclones occurred in 1956, 1969 and 1994, for example, resulted in the total losses of more than 7,400 human lives and enormous economic damage. The most recent data show that the coastal natural hazards on the coast of China have resulted in annual damage of about US $3 billion to the coastal economy and annual loss of 256 human lives. Few researchers in China have quantitatively assessed impacts of storm waves and high wave runup on the coast of China. This study is designated to quantitatively assess impacts of storm wave-induced coastal hazards on the coast of China based on the most recent coastal hazard data (1989-2016). It is found that the combination of storm-induced high surges and large waves is responsible for all major coastal natural hazards and especially for heavy losses of human lives on the coast of China, and that the storm wave-induced hazard intensity increases spatially from the north to south along the coast and well correlates to wave energy flux

Estimating the Values of Missing Data Related to Infrastructure Condition States Using Their Spatial Correlation

Infrastructure managers consistently monitor the condition of their assets to predict their deterioration speed and determine the optimal time to execute preventive interventions. However, despite the recent progress in more frequent and accurate monitoring of assets and storage of the related results, in practice, real-world data often contains errors and discrepancies such as missing data or faulty entries. This problem can happen owing to collection errors during routine inspections or inconsistency of data storage formats in different years. Because the quality of data plays a significant role in the accuracy of deterioration prediction and the resulting intervention programs, it is important to improve condition state predictions by imputing the values of missing information. This paper examines the efficiency of different models that use the spatial correlation of infrastructure assets in predicting the value of missing data. The models include univariate and multivariate Kriging, a hybrid artificial neural network (ANN)-Kriging model, and the bidirectional long short term memory (bi-LSTM) neural network, which can model the data with spatial correlation or a sequential relationship. The results confirm that the condition indicator values can be estimated with reasonably low levels of errorISSN:1076-0342ISSN:1943-555

Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach

The primary objective of this study was to explore the factors that influence metro-bikeshare ridership from a spatial perspective. First, a reproducible method of identifying metro-bikeshare transfer trips was derived using two types of smart-card data (metro and bikeshare). Next, a geographically weighted Poisson regression (GWPR) model was established to explore the relationships between metro-bikeshare transfer volume and several types of independent variables, including sociodemographic, travel-related, and built-environment variables. Moran’s I statistic was applied to examine the spatial autocorrelation of each explanatory variable. The modeling and spatial visualization results show that riding distance is negatively correlated with metro-bikeshare transfer demand, and the coefficient values are generally lower at the edge of the city, especially in underdeveloped areas. Moreover, the density of bus, bikeshare, and other metro stations within 2 km of a metro station has different impacts on metro-bikeshare transfer volume. Travelers whose origin or destination is entertainment related tend to choose bikeshare as a feeder mode to metro if this trip mode is available to them. These results improve our understanding of metro-bikeshare transfer spatial patterns, and several suggestions are provided for improving the integration between metro and bikeshare

Identification of Metro-Bikeshare Transfer Trip Chains by Matching Docked Bikeshare and Metro Smartcards

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics

Identification of Metro-Bikeshare Transfer Trip Chains by Matching Docked Bikeshare and Metro Smartcards

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics

Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable

Plasmonic Core/Satellite Heterostructure with Hierarchical Nanogaps for Raman Spectroscopy Enhanced by Shell-Isolated Nanoparticles

A plasmonic core/satellite heterostructure is synthesized through an electrostatic self-assembly protocol. The heterostructure comprises C-coated Ag nanoparticles known as Ag@C shell-isolated nanoparticles (SHINs) acting as satellite particles and a C-coated Fe3O4 dielectric particle core (Fe3O4@C). The enhanced electromagnetic field generated from the hierarchical nanoscale gaps found among the particles makes high-quality Raman spectra possible. As a result, shell-isolated-nanoparticle-enhanced Raman spectroscopy (SHINERS) based on this heterostructure is successfully applied for in-situ trace-amount detection of perylene in water. There is potential for its use in ultrasensitive detection in the liquid phase with real-time monitoring. This work offers a new strategy for developing SHINs to improve the performance of SHINERS